Technological advances in high-throughput sequencing platforms have made it possible to extend genome-wide association studies (GWAS) to rare variants by whole-genome sequencing and targeted exome-sequencing. Custom chips such as the ImmunoChip and MetaboChip have been utilized for their low cost in genotyping rare variants in candidate regions of interest. Currently most sequencing projects for complex traits are focused on unrelated individuals. Despite the important role the family-based design plays in the rare variant association analysis, there are virtually no general statistical methods developed to analyze rare variant data for complex traits in families. We propose to develop powerful and robust statistical methods to test for association between the joint effects of multiple rare variants in a genomic region of interest and a quantitative trait in family data. e extend the recently developed sequence kernel association test (SKAT) to family data. Our proposed rare variant association methods will have appropriate type I error rates in the presence of family structure and/or population stratification, and will be robust to potential heterogeneity in size and directions of effect in rare variants across a genomic region of interest The statistical significance of association will be assessed analytically, circumventing the difficulty of designing an appropriate permutation procedure in the presence of familial correlation. We plan to examine performance of our proposed methods through large-scale simulation studies under a wide range of realistic scenarios. Our methods will be implemented in freely distributed software, allowing other investigators to apply the methods directly to analysis of their own rare variant data for quantitative traits. PUBLIC HEALTH RELEVANCE: We propose to develop statistical methods and computational tools for the analysis of association between quantitative traits and rare genomic variants in families. Our proposed methods will facilitate the identification of genes contributing to variatio in lipid levels, measures of subclinical atherosclerosis, and many other traits predictive of cardiovascular disease.